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With the deep integration between the unmanned aerial vehicle (UAV) andwireless communication, UAV-based air-to-ground (AG) propagation channels needmore detailed descriptions and accurate models. In this paper, we aim toperform cluster-based characterization and modeling for AG channels. To ourbest knowledge, this is the first study that concentrates on the clustering andtracking of multipath components (MPCs) for time-varying AG channels. Based onmeasurement data at 6.5 GHz with 500 MHz of bandwidth, we first estimatepotential MPCs utilizing the space-alternating generalizedexpectation-maximization (SAGE) algorithm. Then, we cluster the extracted MPCsconsidering their static and dynamic characteristics by employing K-Power-Means(KPM) algorithm under multipath component distance (MCD) measure. Forcharacterizing time-variant clusters, we exploit a clustering-based tracking(CBT) method, which efficiently quantifies the survival lengths of clusters.Ultimately, we establish a cluster-based channel model, and validationsillustrate the accuracy of the proposed model. This work not only promotes abetter understanding of AG propagation channels but also provides a generalcluster-based AG channel model with certain extensibility.

Cluster-based Characterization and Modeling for UAV Air-to-Ground Time-Varying Channels